The present invention relates to rotating machinery and the measurement of strain to determine performance and/or detect malfunctions in rotating machinery. Rotating machinery can use fluid film bearings, which are also called journal bearings, hydrostatic bearings, hydrodynamic bearings, and babbitt bearings. In one embodiment, the present invention relates to systems and methods that measure strain to determine performance and/or detect malfunctions of rotating equipment that uses fluid film bearings.
Strain is a measurement of a material's change in dimension when introduced to stress. The equation for strain is: (δL/L) where δL is the change in length in one dimension of the material from its unstrained state and L is that same unstrained length. Strain in materials is directly proportional to stress, by the equation σ=E*ε where σ is stress in units of pressure, E is Young's Modulus, a value unique to the material and ε is the strain experienced by the material. The units of stress are in force over area, giving a direct relationship between the stress and force applied to the material. Given these three equations, strain in a material having a force applied to it can be directly related to the magnitude of the force. If this force is cyclic, as in rotating and reciprocating equipment, the strain in the material will show magnitude and frequency relatable to the force.
In rotating machinery that uses fluid film bearings, the force experienced by the shaft and bearing can be difficult to measure. The non-linear nature of the fluid in the bearing means that readings taken with an accelerometer rarely display all frequencies in the system and with incorrect magnitude ratios. Proximity probes, which monitor the position of the shaft within the bearing, may indicate issues with running conditions or the shaft itself but do not monitor the health of the rotating machinery itself. By measuring the strain experienced in and/or proximate to the bearing, direct condition monitoring is possible and shows the best possible representation of the force felt by the bearing and related components of a rotating machine during operation.
Manufacturers want to increase profit margin and decrease greenhouse gas emissions. Improvements in production equipment reliability can be a direct path towards achieving these objectives. Equipment efficiency improvement through increased reliability can directly improve the bottom line. Less evident is the impact to emissions that occurs by improperly discarding failed equipment—causing environmental waste—and then purchasing replacement machines—requiring additional energy to produce.
Machinery health monitoring, or condition monitoring, is widely considered one of the best paths towards improving equipment reliability. The customary practice in condition monitoring programs, often used to guide maintenance for large numbers of machines, is vibration acceleration readings to detect faults in machinery. For rotating and reciprocating machinery, vibration analysis methods have been developed to connect vibration signatures to specific components, faults, and operating conditions, but strain has historically not been used. The current state of the art based on vibration requires either an expensive permanent sensor installation or resources to support a portable system in which data are collected and analyzed manually. Sensor installs are often invasive and the portable collection intervals are difficult to manage at an appropriate frequency due to manpower shortage. In both cases, expert analysts must be employed for data interpretation because the current state of the art automated analysis algorithms are inaccurate at best.
Prior art automated analysis tools are inaccurate because the parameters they measure are based on the kinematics (i.e. motion), of the system. These prior art tools read the vibration displacement, velocity, or acceleration of the system, but proper analysis requires an understanding of the input forces. An analyst using measured input kinematics must infer the applied forces based on additional system parameters such as mass, stiffness, and damping. Since every system is different, the path to that inference is inconsistent, and often inaccurate.
Measuring and interpreting strain data puts the analyst much closer to measuring the actual input force. The kinematics of the system are irrelevant since measuring strain essentially bypasses these variables. Measuring strain is a “leapfrog”, so to speak, over the prior art.
Interpreting strain data would significantly improve the automated analysis algorithms and therefore provide an enticement for installing more permanent monitoring systems, which collect more data with more precision than a portable temporary analysis tool. Installations of strain-based measurement tools can be less invasive and therefore more cost effective than the prior art. Wireless data transfer and localized power technology can be more easily justified with strain-based measurement tools than with the prior art tools. Wireless technology, solar energy, thermal power generation, and battery technology are all ready to be adapted to strain-based measurement applications.
Fluid film bearings are widely used. These bearings theoretically have infinite life due to their inherent lubrication. However, error during operation and maintenance can cause fluid film bearings to fail. As an example of a failure mode for a fluid film bearing, consider a main engine bearing that is provided contaminated oil containing wear particles from somewhere else in the engine. These wear particles can score the surface of the bearing, causing increased friction and distortion of the bearing geometry. The added friction can increase the oil temperature, which typically reduces oil viscosity. The deformed geometry and altered viscosity can compromise the hydrodynamic wedge, leading to a change in the pressure distribution of the fluid film bearing. Increased oil temperature can also increase the temperature of the babbitt material. The increased temperature and pressure can cause the babbitt to displace or wipe, further compromising geometry of the fluid film bearing. Eventually there is a chance that the babbitt will become so distorted that it disrupts the formation of any fluid wedge, and the shaft will crash within the fluid film bearing.
Fluid film bearing failures can be difficult to predict, especially when compared to rolling element bearings. Furthermore, successful condition monitoring of fluid film bearings can pose a much greater challenge than for rolling element bearings. Rolling element bearings typically have low internal damping, and solid paths of transmission, which allows:                (a) vibrations to reach the bearing casing linearly;        (b) accelerometers to measure the vibrations of the bearing casing; and        (c) identification of any fault signatures in the resulting accelerometer signal or signals.        
For fluid film bearings, a fluid such as oil or air separates the shaft from the bearing surfaces during normal operation. This fluid film can have much higher damping properties than for rolling element bearings, which can make the system higher order, and non-linear. Therefore, case-mounted accelerometers external to the shaft can be unreliable for monitoring fluid film bearing faults because vibration of the case does not necessarily correlate to shaft vibration in a fluid film bearing. It is therefore desirable not to rely exclusively on accelerometers for fluid film bearing condition monitoring. Other technologies such as temperature trending, proximity sensing using eddy current probes, and/or the use of lasers can also be unfeasible or overly complex or costly for some fluid film bearing applications, and it is desirable not to rely exclusively on these technologies for fluid film bearing condition monitoring.
It is desired to have a more accurate, simpler, and/or lower cost system and method for monitoring fluid film dynamics and the condition of a fluid film bearing in order to improve the prognostics of devices that utilize fluid film bearings.
It should be understood that the drawings are not necessarily to scale. In certain instances, details that are not necessary for an understanding of the invention or that render other details difficult to perceive may have been omitted. It should be understood that the invention is not necessarily limited to the particular embodiments illustrated herein.